A variety of systems are used to measure signals and to process and analyze the signals and provide indications of potential or actual conditions that may be advantageous or disadvantageous. When a potential or actual condition is detected, an alert may be provided and/or an action undertaken.
Such systems may use a classification subsystem to analyze the sampled signals and make a determination regarding characteristics of the signals. Classifiers are broadly applicable to data analysis in a wide array of fields such as, for example, medicine (as in the analysis of physiological signals, medical image analysis, clinical trial analysis), computer vision (as in optical character recognition, face recognition), data mining (as in retail analysis), and communications (as in error detection and correction systems, speech recognition, spam filtering). Classification systems generally accept values, which may or may not be numerical, related to some features or characteristics of a situation and produce as an output some label related to the features or characteristics. For example, a classifier might take as input details about a subject's salary, age, assets, marital status, outstanding debt, and the like and classify the subject as either an acceptable or unacceptable credit risk. As another example, a medical device system might measure electrical signals representative of brain activity and characterize the signals as indicative of an inter-ictal (not seizure) condition or a pre-ictal (pre-seizure) condition.
Classification errors can be troublesome. In the case of medical device systems, classification errors can lead to false positive or false negative indications. Both types of classification errors can be significant and may lead to unnecessary intervention, failure to intervene appropriately, and/or erroneous outputs to the subject, which over time, could reduce the value of the medical device system to the user.